Statistical Analysis in Longitudinal Mental Health Study

描述（由申请人提供）：通常很难或不可能“随机化”治疗（例如，是否指定代表性收款人）或关注的风险因素（例如，社会经济地位），这可能与纵向心理健康结果。因此，观察性研究在精神卫生（服务）研究的许多关键领域发挥着重要作用。然而，在同时存在时间依赖性或纵向混杂的情况下，使用标准方法（例如，使用多元回归来调整基线或时间依赖性差异）可能有偏差。在这项提案中，我们开发了新的分析策略，用于分析纵向心理健康数据，扩展了Robins等人的边际结构框架，以解决基线，最重要的是，纵向混杂协变量可能会使观察到的感兴趣的自变量和结果之间的关系发生偏差的情况。所提出的方法主要用于观察性结局研究，但它们也适用于使用实验设计的研究，例如，当缺失数据量因治疗组而异时，或当发生治疗不依从时。此外，我们提出了潜在类方法的扩展，以允许描述多个纵向变量之间的相互作用模式，从而提高我们对心理健康研究中这些变量之间动态关系的理解。我们要强调本提案中的两个重要概念。首先，我们区分纵向变量和并发时间依赖变量。前者可能只能间歇性地提供，并且可能测量有误差。假设在多个时间点测量的主要关注变量或结果可用时，后者的值是已知的。其次，我们认识到，观察性研究中的因果关系断言必须依赖于“没有不可测量的混杂”这一不可检验的假设。因此，混杂因素的调整只能解决已经测量的因素。因此，我们声明结局“可归因于”定义的风险因素或治疗。我们只是断言我们的发现与因果假设一致，而不是证明因果关系本身。

DESCRIPTION (provided by applicant): it is often difficult or impossible, to "randomize" treatment (e.g. to assign a representative payee or not) or the risk factors of interest (e.g., socioeconomic status) that may be associated with longitudinal mental health outcome. Observational studies thus play an important role in many crucial areas of mental health (services) research. However, in the presence of concurrent time-dependent or longitudinal confounding, statistical analysis with standard approaches (e.g., using multiple regression to adjust for baseline or time-dependent differences) is likely to be biased. In this proposal, we develop new analytic strategies for the analysis of longitudinal mental health data, extending the marginal structural framework of Robins et al, to address situations in which baseline, and most importantly, longitudinal confounding covariates may bias the observed relationships between independent variables of interest and outcomes. The proposed methods are primarily developed for use in observational outcome studies, but they also have applications in studies that use experimental designs as for example, when the amount of missing data varies by treatment group, or when treatment noncompliance occurs. In addition, we propose an extension of the latent class approach to allow description of interacting patterns among multiple longitudinal variables so as to improve our understanding of the dynamic relationship among such variables in mental health research. We want to emphasize two important conceptualizations in this proposal. First, we make the distinction between longitudinal variables and concurrent time-dependent variables. The former may only be available intermittently and may be measured with error. The value of the latter is assumed to be known whenever variables of primary interest or outcomes measured at multiple time points are available. Second, we recognize that assertions of causality in observational study must rely on the untestable assumptions of "no unmeasured confounding". As a result, adjustments for confounding can only address factors that have been measured. Therefore, we state that outcomes are "attributable" to the defined risk factors or treatments. We assert only that our findings are consistent with causal hypotheses, and not that they demonstrate causality itself.